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油画光谱解混成像模型的比较

Comparison of Imaging Models for Spectral Unmixing in Oil Painting.

作者信息

Grillini Federico, Thomas Jean-Baptiste, George Sony

机构信息

The Norwegian Colour and Visual Computing Laboratory, Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.

出版信息

Sensors (Basel). 2021 Apr 2;21(7):2471. doi: 10.3390/s21072471.

Abstract

The radiation captured in spectral imaging depends on both the complex light-matter interaction and the integration of the radiant light by the imaging system. In order to obtain material-specific information, it is important to define and invert an imaging process that takes into account both aspects. In this article, we investigate the use of several mixing models and evaluate their performances in the study of oil paintings. We propose an evaluation protocol, based on different features, i.e., spectral reconstruction, pigment mapping, and concentration estimation, which allows investigating the different properties of those mixing models in the context of spectral imaging. We conduct our experiment on oil-painted mockup samples of mixtures and show that models based on subtractive mixing perform the best for those materials.

摘要

光谱成像中捕获的辐射既取决于复杂的光与物质相互作用,也取决于成像系统对辐射光的积分。为了获取特定材料的信息,定义并反转一个兼顾这两个方面的成像过程很重要。在本文中,我们研究了几种混合模型的用途,并评估了它们在油画研究中的性能。我们基于不同特征,即光谱重建、颜料映射和浓度估计,提出了一种评估方案,该方案允许在光谱成像的背景下研究这些混合模型的不同特性。我们对混合的油画模型样本进行了实验,结果表明基于减法混合的模型对这些材料表现最佳。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3442/8038140/dc3fc4265387/sensors-21-02471-g001.jpg

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